In general fuzzy system, the nature of a fuzzy number is usually viewed as specific and deterministic but, in fuzzy reasoning the membership function is developed in a randomised sense. Here, we have studied the concept of fuzzy approximate reasoning over the modelling of a cost minimization classical economic order quantity (EOQ) inventory management problem. We have developed the model where all parameters assume randomized fuzzy set by means of fuzzy approximate reasoning. First of all, considering the probability density function of the fuzzy variable, utilizing possibility measures of fuzzy sets within we have formulated the expectations of the fuzzy membership functions then we split the model into seven several sub models accordingly. To defuzzify the fuzzy functions the traditional $\alpha$-cuts and its dual k-cuts have been utilized over several feasible spaces of dual spaces of fuzzy variables. Numerical illustrations under different $\alpha$-dual k cuts of the objective functions are done with the help of LINGO software via solution algorithm. Moreover, a comparative study has been done with the existing general fuzzy solution. Managerial insights are also highlighted by showing the superiority of the proposed approach. Finally, sensitivity analysis and graphical illustrations are made to justify the model.

在一般的模糊系统中，模糊数的性质通常被视为特定的和确定性的，但是在模糊推理中，隶属度函数是在随机意义上发展的。在这里，我们研究了在成本最小化经典经济订单数量（EOQ）库存管理问题建模上的模糊近似推理概念。我们已经开发出了一个模型，其中所有参数都通过模糊近似推理假设为随机模糊集。首先，考虑模糊变量的概率密度函数，利用内部模糊集的可能性度量，制定了模糊隶属函数的期望，然后将模型分为七个子模型。为了使模糊函数去模糊，传统的$\alpha$割及其对偶k割已用于模糊变量对偶空间的几个可行空间。不同情况下的数值图示$\alpha$使用LINGO软件通过求解算法对目标函数进行双k割。此外，已经对现有的一般模糊解决方案进行了比较研究。通过显示所提出的方法的优越性，也突出了管理洞察力。最后，进行敏感性分析和图形说明以证明模型的合理性。